<i>DHCR7</i> mutation carrier rates and prevalence of the RSH/Smith‐Lemli‐Opitz syndrome: Where are the patients?
Bibliographic record
Abstract
RSH/Smith-Lemli-Opitz (SLOS) is an inborn error of metabolism with protean manifestations. Its exact incidence and prevalence are not known; however, the carrier rate for the most frequently occurring mutation, the null mutation IVS8-1G > C, is approximately 1 in 100 for the Caucasian population in North America (1%) and possibly as high as 1 in 50 to 1 in 30 in Central European populations (2-3.3%). Based on the allele frequencies and the proportion of this mutation observed in various patient populations, the expected incidence of RSH/SLOS in those populations was calculated and reported to be between 1 in 1,590 and 1 in 17,000. However, around the world the observed prevalence and incidence are much lower than those calculated from the individual mutation carrier rates observed in any given population. The discrepancy between the expected incidence and prevalence can be explained only in part by the neonatal and infancy deaths of the most severely affected children with RSH/SLOS and the under ascertainment of mild and atypical cases at the mild end of the spectrum. RSH/SLOS may be responsible for a high number of miscarriages. Recent observations estimate the prevalence of SLOS at 16 weeks of gestation as similar to that observed at birth (approximately 1 in 60,000) suggesting that either reduced fertility of carrier couples or losses of affected embryos or fetuses in the first trimester play a significant role in reducing the second trimester prevalence of RSH/SLOS. It is possible that the estimates of carrier rates based on population screening for the most commonly occurring mutations may not reflect the true carrier rates in the population. In order to reconcile the above-mentioned paradoxes, we propose a model based on a higher than observed carrier frequency of the most common mutation and on very high fetal loss of homozygotes for that mutation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".